• Journal of Wetlands Research
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• v.24 no.4
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• pp.345-353
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• 2022

A wastewater treatment plant (WWTP) is a major gateway for the engineered nano-particles (ENPs) entering the water bodies. However existing studies have reported that many WWTPs exceed the No Observed Effective Concentration (NOEC) for ENPs in the effluent and thus they need to be designed or operated to more effectively control ENPs. Understanding and predicting ENPs behaviors in the unit and \the whole process of a WWTP should be the key first step to develop strategies for controlling ENPs using a WWTP. This study aims to provide a modeling tool for predicting behaviors and removal efficiencies of ENPs in a WWTP associated with process characteristics and major operating conditions. In the developed model, four unit processes for water treatment (primary clarifier, bioreactor, secondary clarifier, and tertiary treatment unit) were considered. Additionally the model simulates the sludge treatment system as a single process that integrates multiple unit processes including thickeners, digesters, and dewatering units. The simulated ENP was nano-sized TiO₂, (nano-TiO₂) assuming that its behavior in a WWTP is dominated by the attachment with suspendid solids (SS), while dissolution and transformation are insignificant. The attachment mechanism of nano-TiO₂ to SS was incorporated into the model equations using the apparent solid-liquid partition coefficient (Kd) under the equilibrium assumption between solid and liquid phase, and a steady state condition of nano-TiO₂ was assumed. Furthermore, an MS Excel-based user interface was developed to provide user-friendly environment for the nano-TiO₂ removal efficiency calculations. Using the developed model, a preliminary simulation was conducted to examine how the solid retention time (SRT), a major operating variable affects the removal efficiency of nano-TiO₂ particles in a WWTP.

• Journal of the East Asian Society of Dietary Life
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• v.20 no.1
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• pp.20-29
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• 2010

Naringin has antioxidant and antihyperlipidemic properties, however, phenolic compounds including naringin are unstable in the presence of light, heat and oxygen. Beta-cyclodextrin ($\beta$-CD) is a cyclic heptamer composed of seven glucose units that enhances the stability and solubility of molecules through the formation of inclusion complexes. This study was conducted out to compare the effects of CD-naringin (CD-N) inclusion complexes with naringin on lipid metabolism in high fat-fed animals. Male C57BL/6 mice were fed either CD-N (0.048%, w/w) or naringin (N, 0.02%, w/w) in a 20% high-fat (HFC, 15% lard, 5% corn oil, w/w) diet for 10 weeks. Orlistat (Xenical, 0.01%, w/w) was used as a positive control (PC). There were no differences in body weight, food intake, liver and heart weights, plasma triglyceride(TG), leptin, adiponectin, resistin, IL-$1{\beta}$ and IL-6 concentrations, and hepatic $\beta$-oxidation, carnitine palmitoyl transferase(CPT), glucose-6-phosphate dehydrogenase (G6PD) and malic enzyme activities between the HFC and CD-N groups or between the HFC and N groups. However, both CD-naringin and naringin supplementation les to a significant reduction in the epididymal and perirenal white adipose tissue weights, plasma free fatty acid, insulin and blood glucose concentrations, hepatic cholesterol and TG contents and hepatic fatty acid synthase (FAS), phosphatidate phosphohydrolase (PAP) and HMG-CoA reductase activities compared to the HFC group. The plasma HDL-cholesterol concentration was significantly higher in CD-N and N groups than in HF and PC groups. These results indicate that both CD-naringin and naringin supplementation effectively improved plasma and hepatic lipid metabolism without differences between CD-N and naringin groups.

• Journal of the Korean Chemical Society
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• v.39 no.1
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• pp.7-13
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• 1995

The crystal sructures of $X(Ca_{46}Al_{92}Si_{100}O_{384})$ and $Ca_{32}K_{28}-X(Ca_{32}K_{28}Al_{92}Si_{100}O_{384})$ dehydrated at $360^{\circ}C$ and $2{\times}10^{-6}$ Torr have been determined by single-crystal X-ray diffraction techniques in the cubic space group Fd3 at $21(1)^{\circ}C.$ Their structures were refined to the final error indices, R_1=0.096,\;and\;R_2=0.068$ with 166 reflections, and R_1=0.078\;and\;R_2=0.056$ with 130 reflections, respectively, for which I > $3\sigma(I).$ In dehydrated $Ca_{48}-X,\;Ca^{2+}$ ions are located at two different sites opf high occupancies. Sixteen $Ca^{2+}$ ions are located at site I, the centers of the double six rings $(Ca(1)-O(3)=2.51(2)\AA$ and thirty $Ca^{2+}$ ions are located at site II, the six-membered ring faces of sodalite units in the supercage. Latter $Ca^{2+}$ ions are recessed $0.44\AA$ into the supercage from the three O(2) oxygen plane (Ca(2)-O(2)= $2.24(2)\AA$ and $O(2)-Ca(2)-O(2)=119(l)^{\circ}).$ In the structure of $Ca_{32}K_{28}-X$, all $Ca^{2+}$ ions and $K^+$ ions are located at the four different crystallographic sites: 16 $Ca^{2+}$ ions are located in the centers of the double six rings, another sixteen $Ca^{2+}$ ions and sixteen $K^+$ ions are located at the site II in the supercage. These $Ca^{2+}$ ions adn $K^+$ ions are recessed $0.56\AA$ and $1.54\AA$, respectively, into the supercage from their three O(2) oxygen planes $(Ca(2)-O(2)=2.29(2)\AA$, $O(2)-Ca(2)-O(2)=119(1)^{\circ}$, $K(1)-O(2)=2.59(2)\AA$, and $O(2)-K(1)-O(2)=99.2(8)^{\circ}).$ Twelve $K^+$ ions lie at the site III, twofold axis of edge of the four-membered ring ladders inside the supercage $(K(2)-O(4)=3.11(6)\AA$ and $O(1)-K(2)-O(1)=128(2)^{\circ}).$

• Tuberculosis and Respiratory Diseases
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• v.41 no.5
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• pp.452-461
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• 1994

Background: Tumor necrosis factor(TNF)-$\alpha$ and Interleukin(lL)-$1{\beta}$ are thought to play a major role in the pathogenesis of the septic syndrome, which is frequently associated with adult respiratory distress syndrome(ARDS). In spite of many reports for the role of TNF-$\alpha$ in the pathogenesis of ARDS, including human studies, it has been reported that TNF-$\alpha$ is not sensitive and specific marker for impending ARDS. But there is a possibility that the results were affected by the diversity of pathogenetic mechanisms leading to the ARDS because of various underlying disorders of the study group in the previous reports. The purpose of the present study was to evaluate the roles of TNF-$\alpha$ and IL-$1{\beta}$ as a predictable marker for development of ARDS in the patients with septic syndrome, in which the pathogenesis is believed to be mainly cytokine-mediated. Methods: Thirty-six patients of the septic syndrome hospitalized in the intensive care units of the Asan Medical Center were studied. Sixteens suffered from ARDS, whereas the remaining 20 were at the risk of developing ARDS(acute hypoxemic respiratory failure, AHRF). In all patients venous blood samples were collected in heparin-coated tubes at the time of enrollment, at 24 and 72 h thereafter. TNF-$\alpha$ and IL-$1{\beta}$ was measured by an enzyme-linked immunosorbent assay (ELISA). All data are expressed as median with interquartile range. Results: 1) Plama TNF-$\alpha$ levels: Plasma TNF-$\beta$ levels were less than 10pg/mL, which is lowest detection value of the kit used in this study within the range of the $mean{\pm}2SD$, in all of the normal controls, 8 of 16 subjects of ARDS and in 8 in 20 subjects of AHRF. Plasma TNF-$\alpha$ levels from patients with ARDS were 10.26pg/mL(median; <10-16.99pg/mL, interquartile range) and not different from those of patients at AHRF(10.82, <10-20.38pg/mL). There was also no significant difference between pre-ARDS(<10, <10-15.32pg/mL) and ARDS(<10, <10-10.22pg/mL). TNF-$\alpha$ levels were significantly greater in the patients with shock than the patients without shock(12.53pg/mL vs. <10pg/mL) (p<0.01). There was no statistical significance between survivors(<10, <10-12.92pg/mL) and nonsurvivors(11.80, <10-20.8pg/mL) (P=0.28) in the plasma TNF-$\alpha$ levels. 2) Plasma IL-$1{\beta}$ levels: Plasma IL-$1{\beta}$ levels were less than 0.3ng/mL, which is the lowest detection value of the kit used in this study, in one of each patients group. There was no significant difference in IL-$1{\beta}$ levels of the ARDS(2.22, 1.37-8.01ng/mL) and of the AHRF(2.13, 0.83-5.29ng/mL). There was also no significant difference between pre-ARDS(2.53, <0.3-8.34ngfmL) and ARDS(5.35, 0.66-11.51ng/mL), and between patients with septic shock and patients without shock (2.51, 1.28-8.34 vs 1.46, 0.15-2.13ng/mL). Plasma IL-$1{\beta}$ levels were significantly different between survivors(1.37, 0.4-2.36ng/mL) and nonsurvivors(2.84, 1.46-8.34ng/mL). Conclusion: Plasma TNF-$\alpha$ and IL-$1{\beta}$ level are not a predictable marker for development of ARDS. But TNF-$\alpha$ is a marker for shock in septic syndrome. These result could not exclude a possibility of pathophysiologic roles of TNF-$\alpha$ and IL-$1{\beta}$ in acute lung injury because these cytokine could be locally produced and exert its effects within the lungs.

• Journal of the Korean Chemical Society
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• v.37 no.2
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• pp.191-198
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• 1993

The crystal structures of $Cd_{6-}A$ evacuated at $2{\times}10^{-6}$ torr and $750^{\circ}C$ (a = 12.204(1) $\AA$) and dehydrated $Cd_{6-}A$ reacted with 0.1 torr of Cs vapor at $250^{\circ}C$ for 12 hours (a = 12.279(1) $\AA$) have been determined by single crystal X-ray diffraction techniques in the cubic space group Pm3m at $21(1)^{\circ}C.$ Their structures were refined to final error indices, $R_1=$ 0.081 and $R_2=$ 0.091 with 151 reflections and $R_1=$ 0.095 and $R_2=$ 0.089 with 82 reflections, respectively, for which I > $3\sigma(I).$ In vacuum dehydrated $Cd_{6-}A$, six $Cd^{2+}$ ions occupy threefold-axis positions near 6-ring, recessed 0.460(3) $\AA$ into the sodalite cavity from the (111) plane at O(3) : Cd-O(3) = 2.18(2) $\AA$ and O(3)-Cd-O(3) = $115.7(4)^{\circ}.$ Upon treating it with 0.1 torr of Cs vapor at $250^{\circ}C$, all 6 $Cd^{2+}$ ions in dehydrated $Cd_{6-}A$ are reduced by Cs vapor and Cs species are found at 4 crystallographic sites : 3.0 $Cs^+$ ions lie at the centers of the 8-rings at sites of $D_{4h}$ symmetry; ca. 9.0 Cs+ ions lie on the threefold axes of unit cell, ca. 7 in the large cavity and ca. 2 in the sodalite cavity; ca. 0.5 $Cs^+$ ion is found near a 4-ring. In this structure, ca. 12.5 Cs species are found per unit cell, more than the twelve $Cs^+$ ions needed to balance the anionic charge of zeolite framework, indicating that sorption of Cs0 has occurred. The occupancies observed are simply explained by two unit cell arrangements, $Cs_{12}-A$ and $Cs_{13}-A$. About 50% of unit cells may have two $Cs^+$ ions in sodalite unit near opposite 6-rings, six in the large cavity near 6-ring and one in the large cavity near a 4-ring. The remaining 50% of unit cells may have two Cs species in the sodalite unit which are closely associated with two out of 8 $Cs^+$ ions in the large cavity to form linear $(Cs_4)^{3+}$ clusters. These clusters lie on threefold axes and extend through the centers of sodalite units. In all unit cells, three $Cs^+$ ions fill equipoints of symmetry $D_{4h}$ at the centers of 8-rings.

• Korean Journal of Agricultural Science
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• v.4 no.2
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• pp.344-390
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• 1977

The purpose of this paper was to schedule optimum cutting strategy which could maximize the total yield under certain restrictions on periodic timber removals and harvest areas from an industrial forest, based on a linear programming technique. Sensitivity of the regulation model to variations in restrictions has also been analyzed to get information on the changes of total yield in the planning period. The regulation procedure has been made on the experimental forest of the Agricultural College of Seoul National University. The forest is composed of 219 cutting units, and characterized by younger age group which is very common in Korea. The planning period is devided into 10 cutting periods of five years each, and cutting is permissible only on the stands of age groups 5-9. It is also assumed in the study that the subsequent forests are established immediately after cutting existing forests, non-stocked forest lands are planted in first cutting period, and established forests are fully stocked until next harvest. All feasible cutting regimes have been defined to each unit depending on their age groups. Total yield (Vi, k) of each regime expected in the planning period has been projected using stand yield tables and forest inventory data, and the regime which gives highest Vi, k has been selected as a optimum cutting regime. After calculating periodic yields and cutting areas, and total yield from the optimum regimes selected without any restrictions, the upper and lower limits of periodic yields(Vj-max, Vj-min) and those of periodic cutting areas (Aj-max, Aj-min) have been decided. The optimum regimes under such restrictions have been selected by linear programming. The results of the study may be summarized as follows:- 1. The fluctuations of periodic harvest yields and areas under cutting regimes selected without restrictions were very great, because of irregular composition of age classes and growing stocks of existing stands. About 68.8 percent of total yield is expected in period 10, while none of yield in periods 6 and 7. 2. After inspection of the above solution, restricted optimum cutting regimes were obtained under the restrictions of Amin=150 ha, Amax=400ha, $Vmin=5,000m^3$ and $Vmax=50,000m^3$, using LP regulation model. As a result, about $50,000m^3$ of stable harvest yield per period and a relatively balanced age group distribution is expected from period 5. In this case, the loss in total yield was about 29 percent of that of unrestricted regimes. 3. Thinning schedule could be easily treated by the model presented in the study, and the thinnings made it possible to select optimum regimes which might be effective for smoothing the wood flows, not to speak of increasing total yield in the planning period. 4. It was known that the stronger the restrictions becomes in the optimum solution the earlier the period comes in which balanced harvest yields and age group distribution can be formed. There was also a tendency in this particular case that the periodic yields were strongly affected by constraints, and the fluctuations of harvest areas depended upon the amount of periodic yields. 5. Because the total yield was decreased at the increasing rate with imposing stronger restrictions, the Joss would be very great where strict sustained yield and normal age group distribution are required in the earlier periods. 6. Total yield under the same restrictions in a period was increased by lowering the felling age and extending the range of cutting age groups. Therefore, it seemed to be advantageous for producing maximum timber yield to adopt wider range of cutting age groups with the lower limit at which the smallest utilization size of timber could be produced. 7. The LP regulation model presented in the study seemed to be useful in the Korean situation from the following point of view: (1) The model can provide forest managers with the solution of where, when, and how much to cut in order to best fulfill the owners objective. (2) Planning is visualized as a continuous process where new strateges are automatically evolved as changes in the forest environment are recognized. (3) The cost (measured as decrease in total yield) of imposing restrictions can be easily evaluated. (4) Thinning schedule can be treated without difficulty. (5) The model can be applied to irregular forests. (6) Traditional regulation methods can be rainforced by the model.

• Journal of Korean Home Economics Education Association
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• v.26 no.4
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• pp.85-114
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• 2014

The main purpose of this study was to develop teaching and learning plans and a smart textbook for food and nutrition education in Home Economics focusing on 'healthy diet and meal plans' unit in "Technology home Economics" textbooks for 7th graders to evaluate the effectiveness of the instruction conducted with the smart textbook. The content of the study to achieve the purpose is as follows: First, design a smart education-based teaching and learning curriculum for food and nutrition education in Home Economics, focusing on 'healthy diet and meal plans' unit. Second, develop a smart textbook for food and nutrition education based on the teaching and learning curriculum, using a smart content authoring tool. Third, evaluate the effectiveness of the instruction after applying the curriculum in real classroom situations. The results of this study were as follows: First, teaching and learning plans and materials were developed for two units, 'issues regarding teenagers' diet' and 'implementation of a healthy and balanced diet', under 'teenagers' life'. The first unit, 'issues regarding teenagers' diet', dealt with topics such as teenagers' dietary behaviors, nutrition, and health. Learning objectives for this unit were to help students identify and evaluate their own dietary behaviors. The second unit, 'implementation of a healthy and balanced diet', encouraged students to diagnose problems with their diet and plan nutrient rich meals. The objectives for this unit were to help students implement a healthy and balanced diet by providing them with nutrition and dietary guidelines for Koreans, sample meal plans, and guidelines for developing healthy eating habits for teenagers. In order to develop a teaching and learning plans to achieve these objectives, teaching and learning materials including inquiry tasks, materials for group activities, multimedia, applications and various pop-up learning materials were developed. Second, a smart textbook using DocZoom, which was a smart content authoring tool was developed. The textbook dealt with issues regarding teenagers' diet and implementation of a healthy and balanced diet. Multimedia material used in the textbook come from the Ministry of Food and Drug Safety's food and nutrition education web sites and other sources. To develop student-oriented material, relevant video clips were added to the smart textbook to motivate students and enhance their interest in the course. Third, the outcome of this study indicated that the instruction using teaching and learning plans and learning materials with the smart textbook was effective for enhancing students' interest in Home Economics classes (t-value=-3.99, p<.001), creating enthusiasm for learning(t-value = -2.61, p<.05), encouraging self-directed and independent learning(t-value = -4.77, p<.001), and improving students' interest in food and nutrition courses(t-value = -3.83, p<.001). The students' evaluation of the instruction were as follows: the instruction using teaching and learning plans and learning materials with smart textbooks, instead of paper textbooks, helped them save time looking for learning materials; students evaluated that it was easier for them to see and understand video clips and charts. In addition, most students answered that instruction with smart textbooks were more fun and convenient, and they agreed that the courses enhanced their learning experience.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.